def run_training():
initial_steps = 0
max_steps = 10000
batch_size = 56
exp_info = "original"
train_csv_path = "../TACoS/train_clip-sentvec.pkl"
test_csv_path = "../TACoS/test_clip-sentvec.pkl"
test_feature_dir = "../TACoS/Interval128_256_overlap0.8_c3d_fc6/"
train_feature_dir = "../TACoS/Interval64_128_256_512_overlap0.8_c3d_fc6/"
cwd = os.getcwd()
save_model_folder = os.path.join(cwd, "trained_model", exp_info)
if not os.path.isdir(save_model_folder): os.mkdir(save_model_folder)
model = ctrl_model.CTRL_Model(batch_size, train_csv_path, test_csv_path,
test_feature_dir, train_feature_dir)
test_result_output = open("ctrl_test_results.txt", "w")
with tf.Graph().as_default():
loss_align_reg, vs_train_op, vs_eval_op, offset_pred, loss_reg = model.construct_model(
)
# Create a session for running Ops on the Graph.
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.2)
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))
# Run the Op to initialize the variables.
init = tf.initialize_all_variables()
sess.run(init)
saver = tf.train.Saver()
print("Start to test:-----------------\n")
for step in xrange(max_steps):
start_time = time.time()
feed_dict = model.fill_feed_dict_train_reg()
_, loss_value, offset_pred_v, loss_reg_v = sess.run(
[vs_train_op, loss_align_reg, offset_pred, loss_reg],
feed_dict=feed_dict)
duration = time.time() - start_time
if step % 200 == 0:
# Print status to stdout.
print('Step %d: loss = %.3f (%.3f sec)' %
(step, loss_value, duration))
if (step + 1) % 1000 == 0: #2000
saver.save(sess,
save_model_folder + "iter_{}.ckpt".format(step))
print("Start to test:-----------------\n")
movie_length_info = pickle.load(
open("./video_allframes_info.pkl"))
do_eval_slidingclips(sess, vs_eval_op, model,
movie_length_info, step + 1,
test_result_output)
def run_training():
initial_steps = 0
max_steps = 20000
batch_size = 56
train_csv_path = "./exp_data/TACoS/train_clip-sentvec.pkl"
test_csv_path = "./exp_data/TACoS/test_clip-sentvec.pkl"
test_feature_dir = "/home/share/TACoS/Interval128_256_overlap0.8_c3d_fc6/"
train_feature_dir = "/home/share/TACoS/Interval64_128_256_512_overlap0.8_c3d_fc6/"
model = ctrl_model.CTRL_Model(batch_size, train_csv_path, test_csv_path,
test_feature_dir, train_feature_dir)
test_result_output = open("ctrl_test_results.txt", "w")
with tf.Graph().as_default():
loss_align_reg, vs_train_op, vs_eval_op, offset_pred, loss_reg = model.construct_model(
)
# Create a session for running Ops on the Graph.
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.2)
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))
# Run the Op to initialize the variables.
init = tf.initialize_all_variables()
sess.run(init)
for step in range(max_steps):
start_time = time.time()
feed_dict = model.fill_feed_dict_train_reg()
_, loss_value, offset_pred_v, loss_reg_v = sess.run(
[vs_train_op, loss_align_reg, offset_pred, loss_reg],
feed_dict=feed_dict)
duration = time.time() - start_time
if step % 5 == 0:
# Print status to stdout.
print(('Step %d: loss = %.3f (%.3f sec)' %
(step, loss_value, duration)))
if (step + 1) % 2000 == 0:
print("Start to test:-----------------\n")
movie_length_info = pickle.load(open(
"./video_allframes_info.pkl", 'rb'),
encoding='iso-8859-1')
do_eval_slidingclips(sess, vs_eval_op, model,
movie_length_info, step + 1,
test_result_output)
def run_evl():
print('Runing evaluation')
exp = 'test_video_iter9999'
batch_size = 56
train_csv_path = "../TACoS/TACoS/train_clip-sentvec.pkl"
test_csv_path = "../TACoS/TACoS/test_clip-sentvec.pkl"
test_feature_dir = "../TACoS/Interval128_256_overlap0.8_c3d_fc6/"
train_feature_dir = "../TACoS/Interval64_128_256_512_overlap0.8_c3d_fc6/"
model = ctrl_model.CTRL_Model(batch_size, train_csv_path, test_csv_path,
test_feature_dir, train_feature_dir)
test_result_output = open("ctrl_test_results_" + exp + ".txt", "ab")
with tf.Graph().as_default():
loss_align_reg, vs_train_op, vs_eval_op, offset_pred, loss_reg = model.construct_model(
)
sess = tf.Session()
saver = tf.train.Saver()
saver.restore(sess, "trained_model/iter_1999.ckpt")
print("Start to test:-----------------\n")
movie_length_info = pickle.load(open("./video_allframes_info.pkl"))
# do_eval_slidingclips(sess, vs_eval_op, model, movie_length_info, 1999 + 1, test_result_output)
do_eval_slidingclips(sess, vs_eval_op, model, movie_length_info,
1999 + 1, test_result_output)
import requests, json
import numpy as np
import tensorflow as tf
import pickle
import ctrl_model
import os
import cv2
import json
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
model_name = './ckpt/TALL_c3d.ckpt-12000'
video_dir = '/data02/chengjian19/videos/'
app = Flask(__name__)
model = ctrl_model.CTRL_Model()
loss_align_reg, vs_train_op, vs_eval_op, offset_pred, loss_reg = model.construct_model(
)
saver = tf.train.Saver()
sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))
sess.run(tf.global_variables_initializer())
saver.restore(sess, model_name)
def get_visual_featmap(start_center, end_center, feature_center, feature_list):
feature_pre = feature_center[:]
feature_after = feature_center[:]
start_pre, start_after = 0., 100000000.
for clip_name, feature in feature_list:
start = float(clip_name.split("_")[0])
end = float(clip_name.split("_")[1])